A dynamic and spatial scaling approach to advection forecasting

Quantitative nowcasts of rainfall are frequently based on the advection of rain fields observed by weather radar. Major sources of error in this approach are the temporal development of the field during the forecast period, errors in the measured and forecast velocity of the field or rain cell, and the conversion of radar reflectivity into rainfall intensity. Spectral prognosis (S_PROG) is an advection based nowcasting system that uses the observation that rainfields commonly exhibit both spatial and dynamic scaling properties i.e. the lifetime of a feature in the field is dependent on the scale of the feature (large features evolve more slowly than small features) and that features at all scales between the outer and inner observed scales are present in the field. A Fourier notch filter is used to disaggregate the rainfield into a multiplicative hierarchy or cascade of fields, where each level in the cascade represents features in the rainfield at a particular scale. The Lagrangian temporal evolution of each level in the cascade is modelled using a simple autoregressive (lag two) model, which automatically causes the forecast field to become smooth as the structures at the various scales evolve through their life times. This paper describes the model and presents preliminary results from a recent field trial in Sydney as part of the Forecast Demonstration Project of the World Weather Research Programme of the W.M.O.